A Survey of AI Music Generation Tools and Models
This work provides a resource for users from listeners to musicians to understand and choose AI music generation tools, but it is incremental as it surveys existing methods without introducing new ones.
The authors conducted a comprehensive survey of AI music generation tools, classifying them into parameter-based, text-based, and visual-based categories and compiling a list of advantages and limitations to aid in tool selection.
In this work, we provide a comprehensive survey of AI music generation tools, including both research projects and commercialized applications. To conduct our analysis, we classified music generation approaches into three categories: parameter-based, text-based, and visual-based classes. Our survey highlights the diverse possibilities and functional features of these tools, which cater to a wide range of users, from regular listeners to professional musicians. We observed that each tool has its own set of advantages and limitations. As a result, we have compiled a comprehensive list of these factors that should be considered during the tool selection process. Moreover, our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.